Improving the Accuracy of Estimates of Indoor Distance Moved Using Deep Learning-Based Movement Status Recognition
Abstract
:1. Introduction
2. Related Work
2.1. Distance Estimation
2.2. Movement Status Recognition
3. Methodology
3.1. Overview
3.2. Deep Learning-Based Movement Status Recognition
3.3. Distance Estimation
4. Performance Evaluation
4.1. Setup
4.2. Movement Status Classification Performance
4.2.1. Metrics
4.2.2. Classifier Implementation and Training
4.2.3. Comparison with Other Classifying Methods
4.3. Impact of Trace Segment Size
4.4. Distance Estimation Results
4.4.1. Metrics
4.4.2. Comparison with Other Distance Estimating Methods
- ED, calculating the Euclidean distance between the consecutive points of raw trace and using the sum of these distances as the estimated distance;
- KF, utilizing Kalman filter to process the raw trace, and using the distance of processed trace as the estimated distance;
- LSF, utilizing least square fitting to process the raw trace, and using the distance of processed trace as the estimated distance;
- kNN-S, dividing the raw trace into segments, classifying the statuses of segments by kNN, and using status-based estimation to obtain the final distance;
- SVM-S, dividing the raw trace into segments, classifying the statuses of segments by SVM, and using status-based estimation to obtain the final distance;
- CNN-S, dividing the raw trace into segments, classifying the statuses of segments by CNN, and using status-based estimation to obtain the final distance.
5. Conclusions and Future Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Precision | Recall | F1-Score | |
---|---|---|---|
kNN | 83.53% | 82.69% | 83.11% |
SVM | 86.13% | 85.75% | 85.94% |
CNN | 88.05% | 87.76% | 87.90% |
Our classifier | 97.81% | 97.78% | 97.79% |
Training Time | Average Classifying Time for One Segment | |
---|---|---|
kNN | / | 1.32 s |
SVM | 6.3 s | 0.83 s |
CNN | 451 s | 0.0079 s |
Our classifier | 876 s | 0.0079 s |
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Ma, Z.; Zhang, W.; Shi, K. Improving the Accuracy of Estimates of Indoor Distance Moved Using Deep Learning-Based Movement Status Recognition. Sensors 2022, 22, 346. https://doi.org/10.3390/s22010346
Ma Z, Zhang W, Shi K. Improving the Accuracy of Estimates of Indoor Distance Moved Using Deep Learning-Based Movement Status Recognition. Sensors. 2022; 22(1):346. https://doi.org/10.3390/s22010346
Chicago/Turabian StyleMa, Zhenjie, Wenjun Zhang, and Ke Shi. 2022. "Improving the Accuracy of Estimates of Indoor Distance Moved Using Deep Learning-Based Movement Status Recognition" Sensors 22, no. 1: 346. https://doi.org/10.3390/s22010346
APA StyleMa, Z., Zhang, W., & Shi, K. (2022). Improving the Accuracy of Estimates of Indoor Distance Moved Using Deep Learning-Based Movement Status Recognition. Sensors, 22(1), 346. https://doi.org/10.3390/s22010346